{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### best parameter\n",
    "\n",
    "{'randomforestregressor__max_depth': None, 'randomforestregressor__max_features': 'sqrt'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Import libraries and modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    " \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.externals import joblib \n",
    " \n",
    "# 3. Load red wine data.\n",
    "# dataset_url = 'http://mlr.cs.umass.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'\n",
    "# data = pd.read_csv(dataset_url, sep=';')\n",
    "\n",
    "train_data = pd.read_csv('./public.train.csv')\n",
    "test_data = pd.read_csv('./public.test.csv')\n",
    "submit = pd.read_csv('./submit_example.csv') \n",
    "\n",
    "# 4. Split data into training and test sets\n",
    "y = train_data['发电量']\n",
    "X = train_data.drop(['发电量','ID'], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=123, \n",
    "                                                   )\n",
    " \n",
    "# 5. Declare data preprocessing steps\n",
    "pipeline = make_pipeline(preprocessing.StandardScaler(), \n",
    "                         RandomForestRegressor(n_estimators=100))\n",
    " \n",
    "# 6. Declare hyperparameters to tune\n",
    "hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'],\n",
    "                  'randomforestregressor__max_depth': [None, 5, 3, 1]}\n",
    " \n",
    "# 7. Tune model using cross-validation pipeline\n",
    "clf = GridSearchCV(pipeline, hyperparameters, cv=10)\n",
    " \n",
    "clf.fit(X_train, y_train)\n",
    " \n",
    "# 8. Refit on the entire training set\n",
    "# No additional code needed if clf.refit == True (default is True)\n",
    " \n",
    "# 9. Evaluate model pipeline on test data\n",
    "pred = clf.predict(X_test)\n",
    "print (r2_score(y_test, pred))\n",
    "print (mean_squared_error(y_test, pred))\n",
    " \n",
    "# 10. Save model for future use\n",
    "joblib.dump(clf, 'rf_regressor.pkl')\n",
    "# To load: clf2 = joblib.load('rf_regressor.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "clf = joblib.load(\"rf_regressor.pkl\")\n",
    "\n",
    "df_result = pd.DataFrame()\n",
    "df_result['ID'] = list(test_data['ID'])\n",
    "test_feature = test_data.drop('ID', axis=1)\n",
    "pre = clf.predict(test_feature)\n",
    "\n",
    "df_result['Score'] = pre\n",
    "df_result.to_csv('result/submit.csv', index=False, header=False, float_format='%.8f')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>板温</th>\n",
       "      <th>现场温度</th>\n",
       "      <th>光照强度</th>\n",
       "      <th>转换效率</th>\n",
       "      <th>转换效率A</th>\n",
       "      <th>转换效率B</th>\n",
       "      <th>转换效率C</th>\n",
       "      <th>电压A</th>\n",
       "      <th>电压B</th>\n",
       "      <th>电压C</th>\n",
       "      <th>电流A</th>\n",
       "      <th>电流B</th>\n",
       "      <th>电流C</th>\n",
       "      <th>功率A</th>\n",
       "      <th>功率B</th>\n",
       "      <th>功率C</th>\n",
       "      <th>平均功率</th>\n",
       "      <th>风速</th>\n",
       "      <th>风向</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-19.14</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>34</td>\n",
       "      <td>80.55</td>\n",
       "      <td>106.32</td>\n",
       "      <td>16.98</td>\n",
       "      <td>118.36</td>\n",
       "      <td>729</td>\n",
       "      <td>709</td>\n",
       "      <td>725</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.50</td>\n",
       "      <td>976.86</td>\n",
       "      <td>155.98</td>\n",
       "      <td>1087.50</td>\n",
       "      <td>740.11</td>\n",
       "      <td>0.6</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-18.73</td>\n",
       "      <td>-17.3</td>\n",
       "      <td>30</td>\n",
       "      <td>99.90</td>\n",
       "      <td>139.00</td>\n",
       "      <td>21.20</td>\n",
       "      <td>139.51</td>\n",
       "      <td>728</td>\n",
       "      <td>717</td>\n",
       "      <td>726</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1128.40</td>\n",
       "      <td>172.08</td>\n",
       "      <td>1132.56</td>\n",
       "      <td>811.01</td>\n",
       "      <td>0.8</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-17.54</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>41</td>\n",
       "      <td>82.48</td>\n",
       "      <td>114.86</td>\n",
       "      <td>14.91</td>\n",
       "      <td>117.66</td>\n",
       "      <td>731</td>\n",
       "      <td>722</td>\n",
       "      <td>720</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1279.25</td>\n",
       "      <td>166.06</td>\n",
       "      <td>1310.40</td>\n",
       "      <td>918.57</td>\n",
       "      <td>1.1</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-15.43</td>\n",
       "      <td>-16.6</td>\n",
       "      <td>53</td>\n",
       "      <td>73.98</td>\n",
       "      <td>101.72</td>\n",
       "      <td>15.55</td>\n",
       "      <td>104.67</td>\n",
       "      <td>730</td>\n",
       "      <td>727</td>\n",
       "      <td>726</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.31</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1474.60</td>\n",
       "      <td>225.37</td>\n",
       "      <td>1517.34</td>\n",
       "      <td>1072.44</td>\n",
       "      <td>0.9</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-14.60</td>\n",
       "      <td>-16.3</td>\n",
       "      <td>65</td>\n",
       "      <td>64.62</td>\n",
       "      <td>86.86</td>\n",
       "      <td>13.09</td>\n",
       "      <td>93.92</td>\n",
       "      <td>727</td>\n",
       "      <td>729</td>\n",
       "      <td>728</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.30</td>\n",
       "      <td>1548.51</td>\n",
       "      <td>233.28</td>\n",
       "      <td>1674.40</td>\n",
       "      <td>1152.06</td>\n",
       "      <td>1.1</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      板温  现场温度  光照强度   转换效率   转换效率A  转换效率B   转换效率C  电压A  电压B  电压C   电流A   电流B  \\\n",
       "0 -19.14 -17.4    34  80.55  106.32  16.98  118.36  729  709  725  1.34  0.22   \n",
       "1 -18.73 -17.3    30  99.90  139.00  21.20  139.51  728  717  726  1.55  0.24   \n",
       "2 -17.54 -17.0    41  82.48  114.86  14.91  117.66  731  722  720  1.75  0.23   \n",
       "3 -15.43 -16.6    53  73.98  101.72  15.55  104.67  730  727  726  2.02  0.31   \n",
       "4 -14.60 -16.3    65  64.62   86.86  13.09   93.92  727  729  728  2.13  0.32   \n",
       "\n",
       "    电流C      功率A     功率B      功率C     平均功率   风速   风向  \n",
       "0  1.50   976.86  155.98  1087.50   740.11  0.6  272  \n",
       "1  1.56  1128.40  172.08  1132.56   811.01  0.8  275  \n",
       "2  1.82  1279.25  166.06  1310.40   918.57  1.1  283  \n",
       "3  2.09  1474.60  225.37  1517.34  1072.44  0.9  280  \n",
       "4  2.30  1548.51  233.28  1674.40  1152.06  1.1  280  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#coding=utf-8\n",
    "#读取数据\n",
    "import  pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "train_data = pd.read_csv('./public.train.csv')\n",
    "test_data = pd.read_csv('./public.test.csv')\n",
    "submit = pd.read_csv('./submit_example.csv')\n",
    "y = train_data['发电量']\n",
    "X = train_data.drop(['ID','发电量'], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ..., 8997, 8998, 8999])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt_X = np.arange(X.shape[0])\n",
    "plt_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9000,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"板温\"].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 展示 “板温” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"板温\"]).reshape(X[\"板温\"].shape[0],))\n",
    "plt.ylabel('Plate temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 展示“现场温度” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"现场温度\"]).reshape(X[\"现场温度\"].shape[0],))\n",
    "plt.ylabel('Field temperature')\n",
    "#设置坐标轴范围\n",
    "plt.xlim((5889, 5892))\n",
    "# plt.ylim((-2, 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9000.000000\n",
       "mean       -0.629456\n",
       "std        67.430126\n",
       "min     -6321.700000\n",
       "25%        -7.600000\n",
       "50%        -2.400000\n",
       "75%         8.100000\n",
       "max        78.700000\n",
       "Name: 现场温度, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"现场温度\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#过滤异常值，将其变为空值\n",
    "# row_indexs = (data[u'销量'] < 400) | (data[u'销量'] > 5000)  #得到过滤数据的索引\n",
    "# data.loc[row_indexs,u'销量'] = None  #过滤数据\n",
    "X.loc[X[\"现场温度\"]< -1000] = -0.629456"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"现场温度\"]).reshape(X[\"现场温度\"].shape[0],))\n",
    "plt.ylabel('Field temperature')\n",
    "#设置坐标轴范围\n",
    "plt.xlim((5889, 5892))\n",
    "# plt.ylim((-2, 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 展示“光照强度” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"光照强度\"]).reshape(X[\"光照强度\"].shape[0],))\n",
    "plt.ylabel('Light intensity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 展示“转换效率” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"转换效率\"]).reshape(X[\"转换效率\"].shape[0],))\n",
    "plt.ylabel('Conversion efficiency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"转换效率\"]).reshape(X[\"转换效率\"].shape[0],))\n",
    "plt.ylabel('Conversion efficiency')\n",
    "#设置坐标轴范围\n",
    "plt.xlim((2600, 2800))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.loc[X[\"转换效率\"] > 1614] = 0 #异常值用平均值替换 std=807"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"转换效率\"]).reshape(X[\"转换效率\"].shape[0],))\n",
    "plt.ylabel('Conversion efficiency')\n",
    "#设置坐标轴范围\n",
    "# plt.xlim((2600, 2800))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     9000.000000\n",
       "mean        64.068471\n",
       "std        807.425420\n",
       "min          0.000000\n",
       "25%         20.507500\n",
       "50%         25.225000\n",
       "75%         37.190000\n",
       "max      60856.240000\n",
       "Name: 转换效率, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"转换效率\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 展示“转换效率A” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"转换效率A\"]).reshape(X[\"转换效率A\"].shape[0],))\n",
    "plt.ylabel('Conversion efficiency A')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. 展示“转换效率B” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"转换效率B\"]).reshape(X[\"转换效率B\"].shape[0],))\n",
    "plt.ylabel('Conversion efficiency B')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. 展示“转换效率C” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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MLL88o7++CtwFTJD0M+BtwEfKGNOg9s5/uReAtd96T6767/r3+15SfTOzcsoz+us3kpYAZ5B1e30mIraUPTIzM6s4vSYVSfOA64B5EbGn/CGZmVmlynNO5d+A/wWskHSTpPMlDStzXGZmVoHydH/dB9wnqRo4E/gb4Crg2DLHZmZmFSbPiXrS6K/3AX8OnA5c3fMaZmY2GOU5p3ID8GayEWBXAPemuxebmZl1kKel8hPgLyKitdzBmJlZZctzTuUuSW+VNKm4fkRcU8a4zMysAuXp/roWOAV4HGhrrQTgpGJmZh3k6f6aBkyNiCh3MGZmVtnyXKeyHHhZuQMxM7PKl6elcgLZhY+LKLqRZES8v2xRmZlZRcp7Q0kzM7Ne5bqiXtKJwJtS0aKI2FTesMzMrBLleZzwnwGLgA8CfwY8Iun8HOtdJWmTpOVFZaMkLZC0Kr0fn8ol6XJJ9ZKWSjq9aJ3Zqf4qSbOLyt8oaVla53JJfnCYmdkAy3Oi/ovAmyJidkRcCEwHvpRjvZ8CMzqVzQEWRsQUYGGaBzgXmJJeFwM/gCwJAV8hu6J/OvCVtkSU6lxctF7nzzIzs36WJ6lUderu2ppnvYi4H9jWqXgmB+4bdjVwXlH5NZF5GBgp6STgHGBBRGyLiO3AAmBGWnZsRDyUhjpfU7QtMzMbIHlO1N8laT7ZM1Ugu6nknX38vBMjYgNARGyQNDaVjwPWF9VrSGU9lTd0Ud4lSReTtWqYOHFiH0M3M7Pe5DlR/3lJHwDeTvbkx7kR8csSx9HV+ZDoQ3mXImIuMBdg2rRpvojTzKxMuu3GknSqpLcBRMQtEfF3EfFZYKukU/r4eRtT1xXpva1brQGYUFRvPPB8L+Xjuyg3M7MB1NO5ke8Bu7so35uW9cU8oG0E12zg1qLyC9MosDOAnambbD5wtqTj0wn6s4H5adluSWekUV8XFm3LzMwGSE/dX5MiYmnnwoioS3cs7pGk64A/Ak6Q1EA2iutbwI2SLgLWkQ1TBrgDeDdQT5a0Ppo+a5ukrwOLU72vRUTbyf+Pk40wG052jqev53nMzKxEekoqPT2HfnhvG46IWd0sOquLugF8opvtXEX2+OLO5XXA63qLw8zM+k9P3V+LJf1N58LUylhSvpDMzKxS9dRSuQT4paQPcSCJTANqgT8pd2BmZlZ5uk0qEbEReKukP+ZAN9PtEXF3v0RmZmYVJ891KvcA9/RDLGZmVuHy3KbFzMwsFycVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGScVMzMrGSeVQeCu5S/wxq8vYH9L60CHYmZHOCeVw0ChEGx9cX/Ztv/121awdU8Tm3aV7zPMzMBJ5bDwg/ue4Y3f+C0N2/cOdChmZofESeUwcPeTmwDYsLNxgCMxMzs0TiqHAZV7++X+ADOzxEllEIkY6AjM7EjnpHIYKdcffbdUzKy/OKkcBtr+6IebEmZW4ZxUDiNOKWZW6ZxUDgMq+6n6TDhtmVmZOamUWaEQ7GpszlW3c+9XU0uBGxevP+Rusf5KWmZmTipl9q27nuT1X/0Ne/a3dF+pm7/53797FV/4xVJ+vXRDSWLpa2566Jmt/GJJQ0liMLMjm5NKmd3yaPbHeG9T9/fd6q4dsXVPEwA79+Vr6XRnW9oOZC2n/77vmdytJ4BZP3qYz930h0OKwcwGByeVPrjqgTWs2bInV92WQtY8qK7qvgvqkTXbgIPPeRzKqLAv/Wo53/vt0wC8WNRKum/VZr5555N87dcrXvI2zcx646TyEjU2t/K121bwwR8+BMCnr3uMGxevB+Dh1VtZt7Xj/btaW7OE0JfE8FLPhXzy549y1r/dC8C1Dz/L9367qsPyABpTi+nFxh664w4T1y1ax5Yy3mjTzEqv4pOKpBmSnpJUL2lOuT+vLTfs3Jd1Kc37w/N84RdLAbhg7sO841/u6VC/raVSCNjV2Mydy3o4P9Ip7xxoqXQsb2xu5eePrDsoUd22dAPPbO65BdUWT1Uff/I//t1qLr1lWd9WBr726xVc+/CzvdZbs2UPl96yjE/9/LE+f5aZ9b+KTiqSqoErgHOBqcAsSVPL+Zkv7Mpu+njssCG56re2J5XgH29Zxsd/9iirNu5uX16cGDq3ZZpaCu3r7tjbxGW3r6C5tcB3FzzNP/5yGXctf6G97u/rt/QaS0TQ3Jpts7a6bz/6b9y+kusWrevTuht27uOq36/hS79a3mvdtme/tJ0PuuqBNR2S0aQ5t/PNO1Z2ua4vIjUbOBWdVIDpQH1ErI6IJuB6YGY5PmjBio0seXYbi9ZsBeCEEUPZtOvAXYWLu2me3bqHSXNuZ+HKjTSlP+K79jVzWxrFtbrofMzGomecbC06ob5jbxPXp261tVv28LH/t4Qf/W4N/7lwFfevyhLI/as2t9dfvHbbge0UxdKWmCD7A92WFGtSUtnX1NqhDsCj67Z3uQ827+7YFfVg/Rbue3rzQfX2NbXy/I59ACxcuZFJc27n6Y27+fxNS9vrNDa3svy5nUyacztrtuyhsbmVxuZWHl23nV2NzSx/bhcA9ZtfbO9y/NKvllMoBI3NWcL57/tX09JaYFdjM5Pm3E79pt2s27qXyZfewU9/v4aIYMPOfazftpfm1gKFQvDj361m/ba9NDa3srephWUNOykUgn1N2efvTgMYWloL/GH9jvZRe82tBeY/8QKNza1ERPt7m9bCgYRdXNbY3EqhcKAL9P6nN7ev19fkV+qkGREdtrmsYedBx4QNnOJjq6W1cNj/06TDPcCeSDofmBERf53mPwy8OSI+2d0606ZNi7q6upf0OXubWpj65fmHFOvhqKZK7d1hw4ZU0dic/w9JlbIuPchaPSOG1dDUUugwKGD4kGr2NVfe0yarq9TewuxqXjrQJTmkWkhq/yM86uhamlsL7G8ptJdVCYbWdL8vqqtElUAS1cqmqySqUnl1VfYZALsbm9t/TsOGZP8YtJ17a+surZKyREHW3VkoBDXVOugcXVv94pGJQ2uq2J/irq2pav8OtTVVELT/k3RUbTX7Wwq0FoLa6iqGVPd+/k+dbkLXdqwcM7Smh5V63Sy7G1uoraliWE1V1tqPnu9OoYMmssld6Tzj8UcNYW9TK/tbCow6upYqZfux7RjoKqTi76ZO2+2yTofyLqNrL2/7Z25ItWhO52hHH11LIf2MI7JjDGD73uyfopFHHdyT8qZJo/jRhdO6iL53kpZERK6Ve/hpVoSufr4HHU+SLgYuBpg4ceJL/pCjamv4z1mnccPi9bz39Sfxo9+t5tSxIzh59NHUrd3G5BNGUFMlbqhbzytPHMGZrz6RH973DB992yQmHH8Uj63fweija1ny7HaWPbeTWdMnsmtfM4+v38GbXzGKxWu3US1x8TtOYe3WPcy9fzWfPmsKhUJwY916PvbOU9jb1MK//uZpLnnXFIZUV3H5wlV89n+/kqc37uaWR5/ji+9+DU++sJv12/dy9tQT+cnv1/LmyaPYsqeJ+1Nr4mszX8vDq7eyYWcj0yePolriv+59hmOG1TBr+kTm3r8agFe/7Bhe3N9Cw/Z9fPiMk3l6426e27GP//POU7jl0QY27drPn5w2ju/fUw/ArOkTaCkEQ2uqOXpoNc/t2MeaLXt4/bjjqK2p4ke/W8M3P/A/2LBjH5ffna3zDzNezdKGHdy5/AU+deapDK+tJgL+Y+Eqpk8axQOpO2/W9IlMGDWcWx97nqc27uYjb53ECSNq+emDz/KGCcfxunHHsX1PE1c/9CwXvX0yL+xq5PalG3jrKaOZdvLx/KFhJ09v3M1fTJ9IIbJzQm85ZTQ79jWzY28TT298kb/9o1PY1djMojXbGF5bw9tPHc2Q6irm3r+a15x0LKdNGMkxw4bw3/c/w8w3vJwxxwxj1cbdDK2p4uUjh9MawbYXm1i16UVOHTuC4UOqOWpoNcOHVDOkuordjS20FgoUAq5+cC0fnDaBnfuaKBTglSeOoBBZF2drBBHZsO+26dZCUIhICTyQxK59zYw9ZhhDqtV+sB9o/WTJXsp+Oaqr1J4Ui38xiv+ZfPKF3Ty8eiszXncSo4+uZeGTG/njV41leG01Ddv2MbSmirHHDiMieGrjbl4+cjjDh1Sza18z2/Y0cfLooyke3Bgc/IvZ+ZeyEMHaLXt42XHZtrrS2x0gIrJ/+J7dupdXvewYqnQguQp1eSPVtq9dvO2IbH9s29vMMcNqqJZobG6ltRAcNbSaQmT/gFXluDNrd93Zxf+7d/7s3uo/9MwW1m7dy5+ePp7rF69nytgRjB5Ry5Sxx7Tv97bk8tQLu9nV2Mz0yaM6/AyWPreTsccMZX9LK0Nrut7fpVLpLZW3AF+NiHPS/KUAEfHN7tbpS0vFzGwweyktlUo/p7IYmCJpsqRa4AJg3gDHZGY2aFV091dEtEj6JDAfqAauiognBjgsM7NBq6KTCkBE3AHcMdBxmJlZ5Xd/mZnZYcRJxczMSsZJxczMSsZJxczMSsZJxczMSqaiL37sC0mbgd5vk9u1E4De79w4OHhfdOT90ZH3xwFHwr44OSLG5Kk46JLKoZBUl/eq0iOd90VH3h8deX8cMNj2hbu/zMysZJxUzMysZJxUXpq5Ax3AYcT7oiPvj468Pw4YVPvC51TMzKxk3FIxM7OScVLJQdIMSU9Jqpc0Z6DjKRdJEyTdI2mlpCckfSaVj5K0QNKq9H58Kpeky9N+WSrp9KJtzU71V0maPVDf6VBJqpb0mKTb0vxkSY+k73VDeuQCkoam+fq0fFLRNi5N5U9JOmdgvsmhkzRS0s2SnkzHyFsG67Eh6bPpd2S5pOskDRvMx0YHbc+n9qvrF9kt9Z8BXgHUAn8Apg50XGX6ricBp6fpY4CnganAd4A5qXwO8O00/W7gTrIH/Z0BPJLKRwGr0/vxafr4gf5+fdwnfwf8HLgtzd8IXJCmfwh8PE3/LfDDNH0BcEOanpqOmaHA5HQsVQ/09+rjvrga+Os0XQuMHIzHBjAOWAMMLzomPjKYj43il1sqvZsO1EfE6ohoAq4HZg5wTGURERsi4tE0vRtYSfYLNJPsDwrp/bw0PRO4JjIPAyMlnQScAyyIiG0RsR1YAMzox69SEpLGA+8BfpzmBZwJ3JyqdN4XbfvoZuCsVH8mcH1E7I+INUA92TFVUSQdC7wDuBIgIpoiYgeD9Ngge2zIcEk1wFHABgbpsdGZk0rvxgHri+YbUtkRLTXRTwMeAU6MiA2QJR5gbKrW3b45UvbZ94AvAIU0PxrYEREtab74e7V/57R8Z6p/pOyLVwCbgZ+k7sAfSzqaQXhsRMRzwL8C68iSyU5gCYP32OjASaV36qLsiB4yJ2kE8AvgkojY1VPVLsqih/KKIem9wKaIWFJc3EXV6GVZxe+LpAY4HfhBRJwG7CHr7urOEbs/0nmjmWRdVi8HjgbO7aLqYDk2OnBS6V0DMKFofjzw/ADFUnaShpAllJ9FxC2peGPquiC9b0rl3e2bI2GfvQ14v6S1ZF2eZ5K1XEamLg/o+L3av3NafhywjSNjX0D2PRoi4pE0fzNZkhmMx8a7gDURsTkimoFbgLcyeI+NDpxUercYmJJGdtSSnWibN8AxlUXq570SWBkR/160aB7QNkpnNnBrUfmFaaTPGcDO1AUyHzhb0vHpv7qzU1nFiIhLI2J8REwi+5nfHREfAu4Bzk/VOu+Ltn10fqofqfyCNAJoMjAFWNRPX6NkIuIFYL2kV6Wis4AVDMJjg6zb6wxJR6XfmbZ9MSiPjYMM9EiBSniRjWR5mmx0xhcHOp4yfs+3kzW/lwKPp9e7yfp/FwKr0vuoVF/AFWm/LAOmFW3rr8hOPNYDHx3o73aI++WPODD66xVkv/j1wE3A0FQ+LM3Xp+WvKFr/i2kfPQWcO9Df5xD2wxuAunR8/Ips9NagPDaAfwKeBJYD15KN4Bq0x0bxy1fUm5lZybj7y8zMSsZJxczMSsZJxczMSsZJxczMSsZJxczMSsZJxczMSsZJxczMSsbUfxGrAAAADElEQVRJxczMSub/AyHPLMPJkIi5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"转换效率C\"]).reshape(X[\"转换效率C\"].shape[0],))\n",
    "plt.ylabel('Conversion efficiency C')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. 展示“电压A” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"电压A\"]).reshape(X[\"电压A\"].shape[0],))\n",
    "plt.ylabel('Voltage A')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. 展示“电压B” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"电压B\"]).reshape(X[\"电压B\"].shape[0],))\n",
    "plt.ylabel('Voltage B')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. 展示“电压C” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"电压C\"]).reshape(X[\"电压C\"].shape[0],))\n",
    "plt.ylabel('Voltage C')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. 展示“电流A” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"电流A\"]).reshape(X[\"电流A\"].shape[0],))\n",
    "plt.ylabel('Current A')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. 展示“电流B” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"电流B\"]).reshape(X[\"电流B\"].shape[0],))\n",
    "plt.ylabel('Current B')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13. 展示“电流C” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"电流C\"]).reshape(X[\"电流C\"].shape[0],))\n",
    "plt.ylabel('Current C')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. 展示“功率A” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"功率A\"]).reshape(X[\"功率A\"].shape[0],))\n",
    "plt.ylabel('Power A')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15. 展示“功率B” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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mY8xscfh5aCg3M3vazLLNbJaZnZIwT/9Qf7GZ9U8oP9XMZod5nrZwTqm6dewLdNpLRDJVlEc2LwJ9dygbCHzg7p2BD8I4wIVA5/C6DngGYsEB3A2cBnwLuDshPJ4JdbfP13cX62hw1JVaRNJFZGHj7hOAvB2K+wEvheGXgEsTyl/2mM+BFmbWCrgAGOPuee6eD4wB+oZpzd39M4/9xX15h2UlW0daUY6ISENS39dsjnT3tQDh5xGhvDWwKqFeTiirqTwnSXlN69iJmV1nZlPMbEpubu4ev6nI6eybiKS5faWDQLI/p74H5bvF3Z9z9+7u3j0rK2t3Z99tOu0lIpmqvsNmXTgFRvi5PpTnAG0T6rUB1uyivE2S8prWkTLqGCAima6+w2YYsL1HWX/g3YTyq0KvtJ5AYTgFNhroY2aHho4BfYDRYdpmM+sZeqFdtcOykq1DRERSpElUCzaz14CzgZZmlkOsV9nDwFAzuxZYCVweqo8ALgKygWLgGgB3zzOz+4EvQr373H17p4PrifV4OwAYGV7UsA4REUmRyMLG3X9UzaRzk9R14IZqljMEGJKkfArQNUn5xmTrEBGR1NlXOgiIiEgDprBJB+rEJiJpTmEjIiKRU9ikMR3wiEi6UNiIiEjkFDYiIhI5hU090PMDRCTTKWxERCRyChsREYmcwkZERCKnsBERkcgpbEREJHIKmzSkmzlFJN0obOrB3oaDV7MEdakWkXShsNlH1SagdIQjIulCYZMGTMcwIpLmFDYiIhI5hU090HGJiGQ6hY2IiEROYSMiIpFT2IiISOQUNiIiEjmFjYiIRE5hIyIikVPYiIhI5BQ2aaC6Z6PpeTUiki4UNvXIFQ4ikqEUNvXA9AgBEclwChsREYlck1Q3QHa2rayC0orKVDdDRKTO6MhmHzFleR6nP/QBRSXlnPf4R3zznvdT3SQRkTqjsNlH/Gn0QtYWbmN2TiE5+VtT3RwRkTqlsNlHNAq9CFxd1kSkAWqwYWNmfc1soZllm9nAVLdnVxqFT6JSWVNFQXFptdMSg7mi0tlWVhEf31ZWQWFxWXx807ayKvOVh2ti5RWVVCZs9LIdxhPX4e6U1cG1tMRlVtbwgSfWW1OwlTmrCwHI21IaH05maW4R9w+fx/rN22rVltzNJbX+kuPubCj6qn5lpe/WF6TC4rKUfqFy/6q97k5Bcelet6e8opL5azfFx7eVVZCTX1zr+VcXbCV7/eZ4m8prsY+5Oys3Flf5HGrD3Wtdt641yA4CZtYYGAScD+QAX5jZMHefV9frWrRuM5c8/QnfPjaLszq35KrT2/P4+wt5elw2z115Kq9OWslHi3IBmL92E+cefwQPj1zA4nVF/Kp3J1ZsLObuYXPp0PIgAH7+8pSd1vHF8ny6PzCWF67uQacjDqJoWzkAm0vK+XhxLr06teSNKatYt2kb/U9vz4ycAubkFPKdbl/n0fcXcuiBTbnq9HY8MXZRleW2H/geEweew3MTljIzp4DLT23L905pTSMzmjaJpV+yP66j5qzll69MA+D3Fx3HR4ty6XVMS647qyMXP/0JvY5pyV3f6RKvv7ZwK6c/NA6ABff35eGRC1iSW8SgH59Cn8cn0PLgpvy0VwdaHLgffxy1kEp3/nhZNy4d9CkAPdofWqXNAA9c2pV7hs2lvNKZduf5/Oi5z1m+cQv3X9qV4pJy7vlP7KN+4eoeXPePKZRVxH7B/n5Vd259cybNmjRi0I9P4fK/fQbA29efwaatZdz02nQ2l5Tz5A9PYuz8dQyftRaATwb05vpXprF84xYeu7wbfU44imtemMyn2Rv5eEBvjmy+P7NyCjj2qINZW7CNsx/9EICbzjmGW/ocu1P777+0KyVlFTzw3nwAhv/6TLq2PiRe7/vPTGTqinwA/vaTU7jptRmUVlTSpVVzCreWsbpgK31POIpBPz6FsopKjrtzFACXn9qGN6fmADD4k2V8fFtvLvnzJxRuLePDW8+Ot2v7Z9F/yGQmLcuLb4PvPzMRgCd+2I3fvjGTbm1bcM0Z7Xl2wlL+ftWpnPnIeAB+0L0NJ7ZpwWPvL6SxGY/+oBtzcgoZ/OkyzuqcxT3f6cJzE5by7ISlHNi0McN/fSbnPPYRAJ2yDuKpK07m6Q8WsyS3iJ+d1ZHTOx4eb9vQX5zOonWbeebDJZx7/BH8sEdbbv/XbGblFPLODb0YPnMN789bx4mtD+HB73blybGLeXHicm7o3Ylrz+zIKfePAeC1n/fk8K815Tevz+DWPt+gzaEH8uvXpvFl4TZuOrczExZvYMKiXLq0as51/9WRm9+YwS3nf4Obzu3Myo3F9H1qAsWlFcy77wI+X7qRW9+cxZU92/Hb879B/pZSDmzWmKaNG3HMHSPj2/TqM9rz4sTlAHyzzSG8ff0Z7Nf4q+/07s4ZD4/jzku60PeEo3hrWg63vTULgJ+f1YG/f7wMgLsu6cKLE5ezMq+Y/zntaP7w3RNZt2kbU1fkc8EJR3H8naMorajkF9/uyLMfLY0v/8VrenD2sUeQzNrCrVw1eDIr8oq57YJj+XqLA/jVq9Pi+8L++zVOOl9dsYZ42sbMTgfucfcLwvjtAO7+UHXzdO/e3adM2fkP/a50f2AMG4q++vZ9a59v8Oj7X/1Rb9LIKI/4m0TWwc3I3Vyyx/M3sp2PqM7q3JJVecUs31j1G9qAvsfxyKgFVcpaHLgfBQlHEQCfDjyHVs33p7Sikutfmcr4hbm1akurQ/ZnbeGuv5En07HlQSzdsGWX9Q5u1oTNJeW7vfxmTRqxX+NGlFZU8uClXfld+CPRtXVzOh9xMP+evhqA1i0OYHXBV9fdHvn+iezXuBFNmzTixn9OT7rsrIObMfaWb3PIAfsBX4XSdscddTALvtwcHz+j0+FMXLKR+/udwFtTc5iZU/2RTl1rd/iBrAj7Rc+Oh7Eqb2v8/R5+UFM2bqn+aPTMY1oyY1UBRWH7H9+qeZWjgkRtDzuAnPytSW+G7pR1EEtyd/1ZJ/OtDocxOQTseccfyWdLNrCl9Kuj4hev6cHjYxYxqxbb9KS2LZixqqDa6Ref2IrTOh6GAX27tuLFicsYNH4JAE3DvhSVR75/Ip2yvkbu5hJ6H3cE//P3z1m0roiurZvz+dK8KnUn/K43Rx9+4B6tx8ymunv3XdVrqKfRWgOrEsZzQlmd2/6Nefqd5wNUCRqA//z6zKTztUvywX735NZc0aMtEDti+GH3tvFp3zjya0mXc0WPtuRuLiHr4GY8/aOT4+XXn90JgL4nHEXvY7MAeOh7JyZdxk96ttup7OPFG3YKGiAeNM33bxJf7iPf/+ZO9Xo9PI6Ovx/BcXeOYvzC3CrvZUfDbuwVH76/X1eyDm4WHx9/69ncfF7npPPNvfeCKuPvJCynU9ZBVaadfHSL+PC/b+hF+7D9bzrnmCr1hid8Xt87peou84fvnsjQX5xOaXllPGjO73Ikc1ZvigcNxE6LvHBND7IfvJCTj27BgLdnc8vQmfGgeezyblWW+38XH0/u5hJOvu992g98j853jACq3gz82A+68fxVsd/ng5o25tWfncbJR7fgznfn7hQ0f7zsm8y8qw+18d2TW7PsoYuSTju4WfITHyNuOiuhXSfxwKVd4+OT7ziPn5/VAYBubQ5h+cMX87sLYkd253c5kld+dhp3XHx8vP6wG3vFj6KfvfJUJv/+3Pi0cf97Nr897xvxtiR+NmNv+Ta9jjkciH2hW/hAXzofEfsduaF3p2rf7x0XHc8r154WH3/uylP5Ve/YPtDvpK8DcPULX9QqaIB40HRp1TxeNnHgOSx/+GLOPjaL92av5a5353Lnu3Pp8eDYeNAASYPm8R90456EswJ/Tvid3l0D3p7NZX/7jOtfncZxd45i2soCbut7LEOu7hGvs337llfWw60W289hNqQXcDnwfML4lcCfk9S7DpgCTDn66KN9T7z46TJ/9fMV7u7+/twv/YkxC312ToGPm7/Onxq7yCsrK/3JMYu83YDh/ml2rt/+r1k+cvZaLy2v8KfHLvK7353j89cW+n3/mes5+cW+LLfI7/vPXM/dvM0LtpT6aQ+O9Zz8Yi8tr/AOA4f7x4tyfeXGLX7r0Bm+cuMWL6+o9CfGLPQ5qwu8srLSHx29wGeszPetpeX+0Ij5nr1+s+fkF/u9w+b6lpIyX5Zb5H8cNd+XbyjyIZ8s9a53j/KNRSU+f22h//IfU/zZj7J9VV5s+U+NXeQjZ6/xgW/P8nYDhsdfP3n+c5+0dKNvLCrxB9+b5yVlFT47p8B/92asTbNWFfj9/5nrd70z29sNGO4/f+kLz8kv9vwtJT7w7Zn+2ZINvrZgqz86eoFPXZHnlZWVfuGTE7zdgOFeWl7hBcWl/sNnJ/rI2Wvc3b2svMJ/+sJk/9OoBV5eUemvTVrhY+Z+6e7ui9dt8jMe+sA/W7LB3d2nrcirMt+D782Lr6PT7e/5LW/McHf3pblF/ocR87y8otLd3X//r1l+/StT3N39o4Xr/YkxC31jUYkXbSvzQeMX+33/meul5RXu7v7O9Bx/aMR8n7Yiz93dX/hkqb8zPccrKyv9vVlr4p+7u/v6Tdv87nfneO9Hx/sPn53oU5bH5hkz90ufs7rA3d0rKyt90PjFfuvQGf6Ll6f4bW/O9EHjF/vGohJft2mrj5qzNr6/zc4p8FV5W9zdfVlukf/5g0V+yxsz/C/jFvtfx2f73e/Oia/708W5fuXgSV60rcxLyip8SGinu3tpeYUP/WKlb95W5u7u6wq3+sC3Z3pxSbkv+nKTn//4h75pa6m/PHGZf+fPH/tjoxf4r/85zScsWu/u7ou+3OSPjV4QX9c/J63w98NnUlFR6Y+9vzDezsrKSh85e41vKSmLT3909AJfllvk7u7LNxT5g+/N87Kwfd+euir++ZZXVPpjoxf4yo2xZT330RKfsTLf3d0Likv93mFzfd2mre7uvrGoJL6fz11d6O0GDI9v4zcmr/SnEz6XeWsKfe7qwvg63pu1xkvLK7xwa6n/Zdxi/8u4xb5pa6nPW1PoPR4Y42PnfekbNm/zZz/K9nMeHe/LNxT5e7PW+OV/m+hPjV3k7u45+cU+bv66+GdVuLXUb359un8w/0sfOXutPzxyvv9p1ALfWloe+3yyc/3JMbE2VVRU+ocL13tF2B+nrsiLv+c/jJjnv3tzhq/OL/YPF673O/49yz9elOsFW0q93YDh3n/IJK+oqPR/T8vx7g+M8fEL1nm7AcP9tjdnersBw/3CJyf4PcPm+MC3Z3rBllJ3d/9syQb/+4Ql/sWyjf6rV6b6moJi31PAFK/F32WdRgv29DSaiEgmy/TTaF8Anc2sg5k1Ba4AhqW4TSIiGatB9kZz93IzuxEYDTQGhrj73BQ3S0QkYzXIsAFw9xHAiFS3Q0REGu5pNBER2YcobEREJHIKGxERiZzCRkREIqewERGRyDXImzr3hJnlAiv2cPaWwIY6bE660/b4irZFVdoeVTWE7dHO3bN2VUlhUwfMbEpt7qDNFNoeX9G2qErbo6pM2h46jSYiIpFT2IiISOQUNnXjuVQ3YB+j7fEVbYuqtD2qypjtoWs2IiISOR3ZiIhI5BQ2e8nM+prZQjPLNrOBqW5PFMysrZmNN7P5ZjbXzH4Tyg8zszFmtjj8PDSUm5k9HbbJLDM7JWFZ/UP9xWbWP1XvaW+ZWWMzm25mw8N4BzObFN7XG+FfW2BmzcJ4dpjePmEZt4fyhWZ2QfI17fvMrIWZvWVmC8I+cnqG7xu/Db8nc8zsNTPbP5P3j7ja/Ic1var9j6CNgSVAR6ApMBPokup2RfA+WwGnhOGDgUVAF+CPwMBQPhB4JAxfBIwEDOgJTArlhwFLw89Dw/ChqX5/e7hNbgH+CQwP40OBK8Lw34Drw/CvgL+F4SuAN8Jwl7C/NAM6hP2ocarf1x5ui5eAn4XhpkCLTN03iP37+WXAAQn7xdWZvH9sf+nIZu98C8h296XuXgq8DvRLcZvqnLuvdfdpYXgzMJ/YL1U/Yn9oCD8vDcP9gJc95nOghZm1Ai4Axrh7nrvnA2OAvvX4VuqEmbUBLgaeD+MGnAO8FarsuC22b6O3gHND/X7A6+7zyAvMAAACkElEQVRe4u7LgGxi+1NaMbPmwH8BgwHcvdTdC8jQfSNoAhxgZk2AA4G1ZOj+kUhhs3daA6sSxnNCWYMVDvNPBiYBR7r7WogFEnBEqFbddmko2+tJ4DagMowfDhS4e3kYT3xf8fccpheG+g1lW3QEcoEXwmnF583sIDJ033D31cCjwEpiIVMITCVz9484hc3esSRlDbZ7n5l9DXgbuNndN9VUNUmZ11CeNszsEmC9u09NLE5S1XcxLe23RdAEOAV4xt1PBrYQO21WnQa9PcK1qX7ETn19HTgIuDBJ1UzZP+IUNnsnB2ibMN4GWJOitkTKzPYjFjSvuvu/QvG6cAqE8HN9KK9uuzSE7dUL+G8zW07stOk5xI50WoTTJlD1fcXfc5h+CJBHw9gWEHsfOe4+KYy/RSx8MnHfADgPWObuue5eBvwLOIPM3T/iFDZ75wugc+hp0pTYBb5hKW5TnQvnkAcD89398YRJw4DtvYb6A+8mlF8Veh71BArDqZTRQB8zOzR8A+wTytKGu9/u7m3cvT2xz3ucu/8YGA9cFqrtuC22b6PLQn0P5VeE3kgdgM7A5Hp6G3XG3b8EVpnZsaHoXGAeGbhvBCuBnmZ2YPi92b49MnL/qCLVPRTS/UWsd80iYr1F7kh1eyJ6j2cSO4SfBcwIr4uInVv+AFgcfh4W6hswKGyT2UD3hGX9lNjFzmzgmlS/t73cLmfzVW+0jsT+GGQDbwLNQvn+YTw7TO+YMP8dYRstBC5M9fvZi+1wEjAl7B/vEOtNlrH7BnAvsACYA/yDWI+yjN0/tr/0BAEREYmcTqOJiEjkFDYiIhI5hY2IiEROYSMiIpFT2IiISOQUNiIiEjmFjYiIRE5hIyIikft/vHKBKnbELrYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"功率B\"]).reshape(X[\"功率B\"].shape[0],))\n",
    "plt.ylabel('Power B')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16. 展示“功率C” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"功率C\"]).reshape(X[\"功率C\"].shape[0],))\n",
    "plt.ylabel('Power C')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17. 展示“平均功率” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"平均功率\"]).reshape(X[\"平均功率\"].shape[0],))\n",
    "plt.ylabel('Average power')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18. 展示“风速” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"风速\"]).reshape(X[\"风速\"].shape[0],))\n",
    "plt.ylabel('Wind speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19. 展示“风向” 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt_X = np.arange(X.shape[0])\n",
    "plt.plot(plt_X,(X[\"风向\"]).reshape(X[\"风向\"].shape[0],))\n",
    "plt.ylabel('wind direction')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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